This research investigates the micro-aggregation problem in secure statistical databases by integrating the divide and conquer concept with a genetic algorithm. This is achieved by recursively dividing a micro-data set into two subsets based on the proximity distance similarity. On each subset the genetic operation “crossover” is performed until the convergence condition is satisfied. The recursion will be terminated if the size of the generated subset is satisfied. Eventually, the genetic operation “mutation” will be performed over all generated subsets that satisfied the variable group size constraint in order to maximize the objective function. Experimentally, the proposed micro-aggregation technique was applied to recommended real-life data sets. Results demonstrated a remarkable reduction in the computational time, which sometimes exceeded 70% compared to the state-of-the-art. Furthermore, a good equilibrium value of the Scoring Index $\left(SI\right)$ was achieved by involving a linear combination of the General Information Loss $\left(G_{IL}\right)$ and the General Disclosure Risk $\left(G_{DR}\right)$.

Page replacement algorithms play a crucial role in enhancing the performance issue brought on by variations in processor speeds and memory by effectively removing pages from computer memory to improve overall efficiency. The majority of these algorithms can address the page replacement problems, but their implementation is challenging. This paper introduces a new efficient page replacement algorithm, i.e., enhanced least-replacement (E-LRU) based on two introduced features used to select the victim page. By incorporating elements of traditional algorithms such as first in first out (FIFO) and least recently used (LRU), E-LRU presents itself as a new approach with potential benefits for memory management. This study evaluates the effectiveness of E-LRU in reducing power consumption by reducing cache faults and compares its performance to existing algorithms in various settings. The results provide insight into the advantages and disadvantages of E-LRU and essential perspectives on its potential benefits for contemporary memory management algorithms. Furthermore, the study puts E-LRU into the perspective of evolving algorithms and provides directions for future investigation and improvement in the ever-changing field of memory management. The study proved that E-LRU works better than FIFO and LRU algorithms.

In this paper, we present deep learning models for classifying ancient-coins using transfer learning applied to a custom dataset curated from a specialized collection. The dataset was meticulously developed through rigorous preparation and quality screening; the retained fine-grained classes are imbalanced. We initially performed binary classification before progressing to separate fine-grained assessments of DenseNet121, EfficientNetB0, EfficientNetV2S, and ResNet50. In confirmatory experiments using a physical-coin-aware 70/15/15 split and five training seeds, the coarse CITY-versus-NABATAEAN task remained approximately perfectly separable. Fine-grained performance was lower: ResNet50 achieved 68.28 ± 2.45% CITY accuracy, whereas DenseNet121 achieved 72.89 ± 2.79% NABATAEAN accuracy. These results show that near-perfect coarse classification does not by itself imply reliable fine-grained attribution. This analysis underscores the importance of meticulous dataset preparation and strategic model selection in optimizing performance. A matched DenseNet121 ablation further showed that ImageNet initialization substantially improved the difficult fine-grained tasks compared with random initialization. The proposed framework supports digital documentation and analysis of ancient coin collections, while the current single-collection evaluation still limits claims of cross-collection generalization.